R Notebook

library("plotly")

Loading required package: ggplot2
Want to understand how all the pieces fit together? Read R for Data Science: https://r4ds.had.co.nz/
Registered S3 method overwritten by 'data.table':
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Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

library("plot3D")
library(tidyverse) # entorno tidy

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks plotly::filter(), stats::filter()
x dplyr::lag()    masks stats::lag()

library(dplyr) # manejo de datos
library(GGally) # scatterplots multiples

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

library(rgl) # para graficos 3D

df = read.csv("chicos.csv", stringsAsFactors = F)

indices = (
  ! is.na(df$WEIGHT) &
  ! is.na(df$STATURE) &
  ! is.na(df$SEX) &
  ! is.na(df$RACE) &
  ! is.na(df$AGE.IN.YEARS) &
  ! is.na(df$AGE.IN.MONTHS) &
  df$WEIGHT>0 &
  df$STATURE>0 &
  df$RACE != 0 &
  (df$SEX == 1 | df$SEX==2)
    )
dfFiltrados = df[indices,]

```r
unique(df$SEX)

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[1] 2 1

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```r
range(dfFiltrados$AGE.IN.YEARS)

[1]  2015 20054

View(dfFiltrados)

plot(dfFiltrados$WEIGHT/10, dfFiltrados$STATURE/10, xlab = "Peso en KG", ylab = "Altura en CM", col=dfFiltrados$SEX)

plot(dfFiltrados$AGE.IN.MONTHS/12, dfFiltrados$STATURE/10, col=dfFiltrados$SEX, xlab = "Edad (anios)", ylab = "Altura en CM",)

peso = dfFiltrados$WEIGHT/10
edad = dfFiltrados$AGE.IN.MONTHS/12
altura = dfFiltrados$STATURE/10
sexo = dfFiltrados$SEX
colores = c('blue','red')

fig <- plot_ly(x=~altura, y=~edad, z=~peso, marker = list(color = colores[dfFiltrados$SEX], showscale = F) , type="scatter3d", mode="markers", col=sexo, size = 1)
fig <- fig %>% layout(title = 'Peso en funcion de altura y edad'
         )

fig

Warning: 'scatter3d' objects don't have these attributes: 'col'
Valid attributes include:
'connectgaps', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'error_z', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'projection', 'scene', 'showlegend', 'stream', 'surfaceaxis', 'surfacecolor', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'visible', 'x', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'ycalendar', 'yhoverformat', 'ysrc', 'z', 'zcalendar', 'zhoverformat', 'zsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'

Warning: 'scatter3d' objects don't have these attributes: 'col'
Valid attributes include:
'connectgaps', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'error_z', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'projection', 'scene', 'showlegend', 'stream', 'surfaceaxis', 'surfacecolor', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'visible', 'x', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'ycalendar', 'yhoverformat', 'ysrc', 'z', 'zcalendar', 'zhoverformat', 'zsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'

<<<<<<< HEAD

dfFiltrados$AGE.IN.YEARS

   [1]  4219  4326  4476  3841  3460  3723  3394  3608  5580  5230  4893 13561 13736 13953 13397 13345 12539 12594 12413
  [20] 13884 12868 12772 11928 12706 12767 12095 14471 13550 12145 12199 13832 12312 12917 13364 14213 13452 13523 13915
  [39] 13731 14309 12950 13726 12906 13926 13657 12619 13635 13953 13038 12098 12235 12712 10134 10079  5976  6531 11723
  [58]  5761  5024  8775  9109 10079  7320  6893 11197  8693  8197  7197  7676  8841  8482  7580  7361 11980  5073  6767
  [77]  6802  4989  8394  8808  8838  8000 11345  9723 10473 11498 12221 10712 10542  9750 10073 10715 12975 11372 12172
  [96] 11205 11024 11731 11835 11873  9695  9027  9830 10087 10657 10284 10383 11282 11060 11857 11520  5145 15605 16654
 [115] 17402 17504 17920 17356 18600 16986 17347 16734 18539 17668 16254 15082 15071 16676 16517 14610 14356 15468 15526
 [134] 16920 17463 15693 16158 16010 18060 17602 15761 15693 17257 18021 16202 15956 14843 14800 17254 15709 16024 20054
 [153] 14917 16983 14832 14265 15205 15747 15002 18641 16180 16978 16473 17452 14345 16243 17668 16339 16643 17868 15430
 [172]  4961  4356  4632  3972  2854  4180  3569  3939  6498  8482  8715  8241  8164  6224  6961  8315  8369  8745  7000
 [191]  6786  6846  6205  6427  7638  5947  5750  6071  6506  7361  8704  7512  6600  6654  9621  7621  7961  7569  7838
 [210]  7397  6323  8254  8282  8569  7254  5523  5980  6912  7986  8789  7131  8726  6090  7482  7227  5786  5331  6238
 [229]  7441  5520  3786  4846  5057  4684  3731  3432  2852  2684  4408  5679  4605  4284  5964  3126  4441  4172  4690
 [248]  4145  4430  4654  4597  4800  4443  4030  5027  4432  3668  3115  3008  4076  4194  4843  4643  4608  4153  3054
 [267]  5276  4901  7539  8120 11556 17545 17235 13038 14441 14326 15657 17991 12931  7430  5402  4780 14375 14912 13863
 [286] 14241 11734  3953  5531 12594  7380  7808  9512  9819 10920 10641 11901 11389  9476  9682  5690  7523  5720  6153
 [305] 11890 11841  7068  6336  8517  8690  5520  5189  9008  4789  7213 13199 11323  2073  6257  3126  9906  8276  5101
 [324] 14073 13854 10038 11964  2238  4504  9652  9953 10386  7610  8019  8104 11076 10657 10358 10843 10506  8608  9093
 [343]  8515  6167  5947  7238  6399  7284  5841  5306  6964  6580  9241  9241 10386 10917 10720  9254  7405  9230  8156
 [362]  8487  8780  9019  8969  8989  9008  8498 11931 11391  5887  6115  6246  6843  7263  7430  7863 11169 10676 10410
 [381] 10364  7394  7435  9810 10254  9334  9621  5386  5975  7723  7506 12057 12936  6769  6427  8652 11232 11742 12312
 [400] 11452 12572  9490  6865  5586  5410 14317 13665 13419  9873  7361  9852 13630 12526 13093 13252 11336 11224 12624
 [419] 14221 12556 12594 12408 12854 11334 11347 11912 12591 11610  7613  7539  9317 10043 10742  9484  9832 10421 10219
 [438] 12495  7219  6528 12471 13219 13139 11079 10610 10216 11652  9041  9210  6684  7153 10824 10610 10731 10736  5838
 [457]  6139 11520 11871 10435 10997  5821  5273  7178  6441  6216  9397  9515  5446  6049 10495 10838 11167  8052  8279
 [476] 12490  5645  5484  5235  5441  5438  6167  6189  5578  7547  3704  6290  4873  5432  3783  7698  8460  9257  9734
 [495] 11917  6591  8010  6802 10073 11884 11945 11410 12109  6331  6882  7654 11284 11413 12054 11454 12134 11575 11232
 [514] 13652 14202 13715 14608 13632 14035 13657 13575 13216 13356 12600 12523 12739 13931 13465 13687 13616 13539 14156
 [533] 12164 11306 11547 11931 12093  7041  6282  9915 10473 12104  5405 11252  7539  7515 10769  6482  6304  7720  7328
 [552]  7931  8695  9479  9399 11084 11150 12054 10569 10243 10742 10375  5424  5832  6545  6778 11495 10909 10646  6271
 [571]  9821 11241  9479  7665 10076 11191 10260 11082  8594 11419 10317 10821 10358  7339 11854 11668  6002  5463  6167
 [590]  7106 10290 10942  6939  6506  9410 10038 11375 11827  6115  9695  9358  9610  9578  9665  9745 10756  9904  8778
 [609]  8854  8641  7397  7956  7704  8457  9369 10271  8824  9760  9761  9465 10071 10734  9728  9797  2597  5695  6013
 [628]  4600  3767  2495  2857  3364  4734  2243  4197  4079  4079  2424  2983  2054  3487  3824  4287  3671  4895  3621
 [647]  3986  2320  2465  2386  3638  3504  3008  4227  3397  3586  3290  2032  2547  2241  3468  2383 13367 13386 13942
 [666] 14457 13295 13750 13304 14268 13391 12578 12498 15424 13301 13997 14745 13504 13082 13183 13509 13238 12621 13463
 [685] 13265 13857 13789 13531 13856 14254 14750 14372 14268 13602 13728 13575  4838  3983  3868  5315  4747  4079  2175
 [704]  3769  3550  3361  6224  5726  3257  2482  2367  2539  2849  2863  4246  2841  2857  2786  5983  5452 18126 18213
 [723] 18038 17956 15742 16515 17334 14673 17616 17147 17975 17035 14534 15183 15989 15967 14816 14309 16186 14736 15994
 [742] 14320 15145 15073 18265 16383 15238 14482 16487 16515 15323 15408 15194 15558 17778 18147 16753 16597 16712 16460
 [761] 18073 18947 17457 16320 15419 15104 17701 17473 14756 13663 14454 13542 13975 14197 13260 12468 13750 14778 14194
 [780] 12780 12435 13391 12361 12306 14000 13361 12501 13534 12336 13134 12641  7087  6789  7279  6512  6558  6813  8063
 [799]  7712  6452  6323  7339  8317  7452  7934 11624 12356 11873 11134  9315  8800  8506  8534  8876  8854  8879  8720
 [818] 10668  9506 10873  9860 10309  9747  6334  6632 15372 16268 14591 16945 14430 15287 15043 15545  8827  8013  7734
 [837] 11241 10906 10805  7336  9263  6523  6671  6608 17367 15210 15126 17265 18383 14912 15115 14890 15276 14383 16167
 [856] 14542 14454 15079 15358 16465 14969 14619 15172 15038 17224 14367 14591 17926 16156 18276 11964 11068 16221 18602
 [875] 18424 18219 16865 17558 17501 16227 17309 16441 16219 19194 18189 18208 17397 16654 16556 16605 17926 16915 16778
 [894] 18295 16761 17972 18273 17397 18778 18879 17882 16632  9399 11246 11172  7336  8852 11873 10860 12287  9441  8427
 [913] 18098  9430  9241  9671  9408  8961 10221 17098 15863 15709 17413 17287 15673 15923 18254 16939 14723 16106 18049
 [932] 17671 16652 16397 14706 14813 15926 15227 17476 14906 17399 15550 16268 14964 17476 15690 14876 16038 15358 14778
 [951] 15060 16369 15671 16600 17117 18915 16564 16520 16052 15367 15032 15049 15073 15345 15660 14876 16802 15761 16035
 [970] 16268 16328 14416 13772 13123 11994 12284 13032 13123 13189 12260 13243 14095 12216 13095 14164 12632 14479 12504
 [989] 12399 14665 13682 12276 11810 12756 13498 10293 12109 12126 11632 10726
 [ reached getOption("max.print") -- omitted 2884 entries ]

range(dfFiltrados$AGE.IN.MONTHS)

[1]  24 240

range(dfFiltrados$AGE.IN.YEARS)*12/1000

[1]  24.180 240.648

=======

table(dfFiltrados$RACE)

   1    2    3    4    5 
3370  426   31    6   51 

>>>>>>> refs/remotes/origin/main

#Cinco niveles, WHITE = 1, BLACK = 2, ORIENTAL = 3, AMERICAN INDIAN = 4, MIXED = 5.
#Absoluta disparidad de muestreo en cuanto a raza.

table(dfFiltrados$SEX)

   1    2 
1972 1912 

#En cuanto a sexo no

Las personas mas pesadas, desarollan piernas mas anchas para soportar su peso que alguien de su misma altura menos pesada?

Podemos identificar a las personas con enanismo? ademas de menor altura para su edad, que otras variables tienen fundamentalmente distintas?

A que edad pegan “el estiron” los hombres y cuando las mujeres? existe un momento concreto? Ademas de altura, que otras medidas cambian considerablemente? (medida de cintura en mujeres por ej, ensanchamiento de espalda hombres?)

<<<<<<< HEAD ======= >>>>>>> refs/remotes/origin/main